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Multi-target Detection based on Multi-sensor Redundancy and Dynamic Weight Distribution for Driverless Cars

  • Automotive Engineering College

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The perception system of unmanned vehicles has developed rapidly in recent years, but in the process of multisensor fusion, a certain sensor's perception error often occurs which causes great errors in the perception results of the entire multi-sensor system and is harmful to human life and property. Therefore, it requires redundant fusion of the objects perceived by the sensors, and sets weights during the fusion process to eliminate sensors with poor detection results. This paper establishes a multi-sensor perception and a dynamic weight distribution and eliminate system. Multi-sensor weight distribution is performed by Kalman tracking variance. Such a system will greatly reduce the errors of the perception system.

Original languageEnglish
Title of host publication2021 IEEE 3rd International Conference on Communications, Information System and Computer Engineering, CISCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages229-234
Number of pages6
ISBN (Electronic)9780738112152
DOIs
StatePublished - 14 May 2021
Externally publishedYes
Event3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021 - Beijing, China
Duration: 14 May 202116 May 2021

Publication series

Name2021 IEEE 3rd International Conference on Communications, Information System and Computer Engineering, CISCE 2021

Conference

Conference3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021
Country/TerritoryChina
CityBeijing
Period14/05/2116/05/21

Keywords

  • dynamic weight distribution
  • multi-sensor redundancy
  • multi-target detection

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